Salīdzināt metodes

	Beijes tīkls ar mērīšanas kļūdu	Latent Class Analysis (LCA)
Nozare	Bajesa metodes	Statistika
Saime	Bayesian methods	Latent structure
Izcelsmes gads	1988 (Bayesian networks); measurement-error extension: 1990s	1950s–1968
Autors	Judea Pearl (Bayesian networks); measurement-error extension developed in epidemiology and psychometrics through the 1990s–2000s	Paul F. Lazarsfeld
Tips	Probabilistic graphical model with latent variables	Latent variable / person-centered classification
Pirmavots	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
Citi nosaukumi	BN-ME, errors-in-variables Bayesian network, Bayesian graphical model with measurement error, latent variable Bayesian network	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
Saistītās	5	6
Kopsavilkums	A Bayesian network with measurement error is a probabilistic directed acyclic graphical model in which one or more node variables are observed with error rather than exactly. Latent true-value nodes are introduced for mismeasured variables, and the model jointly infers the network's conditional probability parameters and the unobserved true values from the noisy observations.	Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data.